Overview

Dataset statistics

Number of variables34
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory390.6 KiB
Average record size in memory272.1 B

Variable types

Numeric14
Boolean3
Categorical17

Alerts

Date_of_Hire has a high cardinality: 1112 distinct values High cardinality
Age is highly correlated with TotalWorkingYearsHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
year is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
Age is highly correlated with JobLevel and 1 other fieldsHigh correlation
JobLevel is highly correlated with Age and 4 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 3 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly correlated with JobLevel and 5 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
year is highly correlated with JobLevel and 5 other fieldsHigh correlation
Age is highly correlated with TotalWorkingYearsHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
year is highly correlated with TotalWorkingYears and 2 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevelHigh correlation
JobRole is highly correlated with JobLevel and 1 other fieldsHigh correlation
JobLevel is highly correlated with JobRoleHigh correlation
Department is highly correlated with JobRoleHigh correlation
StockOptionLevel is highly correlated with MaritalStatusHigh correlation
Age is highly correlated with JobLevel and 3 other fieldsHigh correlation
Department is highly correlated with JobRoleHigh correlation
JobLevel is highly correlated with Age and 5 other fieldsHigh correlation
JobRole is highly correlated with Department and 3 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly correlated with Age and 4 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly correlated with Age and 7 other fieldsHigh correlation
YearsAtCompany is highly correlated with Age and 6 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with JobLevel and 4 other fieldsHigh correlation
year is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
Date_of_Hire is uniformly distributed Uniform
NumCompaniesWorked has 197 (13.4%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros
Leaves has 243 (16.5%) zeros Zeros

Reproduction

Analysis started2021-11-26 09:10:21.939073
Analysis finished2021-11-26 09:11:03.578052
Duration41.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:03.707874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
MonotonicityNot monotonic
2021-11-26T14:41:03.848507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3858
 
3.9%
3358
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1233 
True
237 
ValueCountFrequency (%)
False1233
83.9%
True237
 
16.1%
2021-11-26T14:41:03.957861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Length

Max length17
Median length13
Mean length13.44761905
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely1043
71.0%
Travel_Frequently277
 
18.8%
Non-Travel150
 
10.2%

Length

2021-11-26T14:41:04.051543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:04.145309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely1043
71.0%
travel_frequently277
 
18.8%
non-travel150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.54217687
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowResearch & Development
4th rowSales
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development961
65.4%
Sales446
30.3%
Human Resources63
 
4.3%

Length

2021-11-26T14:41:04.270238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:04.363970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
research961
27.8%
961
27.8%
development961
27.8%
sales446
12.9%
human63
 
1.8%
resources63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:04.457695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
MonotonicityNot monotonic
2021-11-26T14:41:04.598331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Male
882 
Female
588 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male882
60.0%
Female588
40.0%

Length

2021-11-26T14:41:04.738926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:04.832654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male882
60.0%
female588
40.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2021-11-26T14:41:04.935784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:05.013847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Length

2021-11-26T14:41:05.138817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:05.232589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobRole
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length18
Mean length18.0707483
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowResearch Scientist
3rd rowResearch Director
4th rowSales Representative
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive326
22.2%
Research Scientist292
19.9%
Laboratory Technician259
17.6%
Manufacturing Director145
9.9%
Healthcare Representative131
8.9%
Manager102
 
6.9%
Sales Representative83
 
5.6%
Research Director80
 
5.4%
Human Resources52
 
3.5%

Length

2021-11-26T14:41:05.373133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:05.482530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sales409
14.4%
research372
13.1%
executive326
11.5%
scientist292
10.3%
laboratory259
9.1%
technician259
9.1%
director225
7.9%
representative214
7.5%
manufacturing145
 
5.1%
healthcare131
 
4.6%
Other values (3)206
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row1
4th row1
5th row4

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2021-11-26T14:41:05.685606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:05.904262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaritalStatus
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Married
673 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.902721088
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowDivorced
5th rowSingle

Common Values

ValueCountFrequency (%)
Married673
45.8%
Single470
32.0%
Divorced327
22.2%

Length

2021-11-26T14:41:06.029273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:06.138579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married673
45.8%
single470
32.0%
divorced327
22.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:06.263546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
MonotonicityNot monotonic
2021-11-26T14:41:06.414299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
27413
 
0.2%
26103
 
0.2%
61423
 
0.2%
24043
 
0.2%
25593
 
0.2%
34523
 
0.2%
63473
 
0.2%
55623
 
0.2%
24513
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:06.554942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
MonotonicityNot monotonic
2021-11-26T14:41:06.648624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False1054
71.7%
True416
 
28.3%
2021-11-26T14:41:06.726774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:06.811069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659937717
Coefficient of variation (CV)0.2406346025
Kurtosis-0.3005982221
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8211279756
Sum22358
Variance13.39514409
MonotonicityNot monotonic
2021-11-26T14:41:06.920377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

PerformanceRating
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Length

2021-11-26T14:41:07.074270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:07.152384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

StockOptionLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Length

2021-11-26T14:41:07.246156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:07.339840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalWorkingYears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:07.464811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
MonotonicityNot monotonic
2021-11-26T14:41:07.636646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:07.777276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289270621
Coefficient of variation (CV)0.4605656896
Kurtosis0.494992986
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5531241711
Sum4115
Variance1.662218734
MonotonicityNot monotonic
2021-11-26T14:41:07.870961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

YearsAtCompany
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:08.011554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
MonotonicityIncreasing
2021-11-26T14:41:08.152190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:08.292786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
MonotonicityNot monotonic
2021-11-26T14:41:08.417711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:08.542723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
MonotonicityNot monotonic
2021-11-26T14:41:08.667693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

Higher_Education
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Post-Graduation
387 
Graduation
367 
PHD
358 
12th
358 

Length

Max length15
Median length10
Mean length8.150340136
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowPost-Graduation
4th rowPHD
5th rowPHD

Common Values

ValueCountFrequency (%)
Post-Graduation387
26.3%
Graduation367
25.0%
PHD358
24.4%
12th358
24.4%

Length

2021-11-26T14:41:08.931290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:09.025016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
post-graduation387
26.3%
graduation367
25.0%
phd358
24.4%
12th358
24.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Date_of_Hire
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1112
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
27-06-2020
 
5
17-02-2016
 
5
08-02-2018
 
4
10-01-2016
 
4
13-02-2013
 
4
Other values (1107)
1448 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique824 ?
Unique (%)56.1%

Sample

1st row21-01-2021
2nd row13-03-2021
3rd row23-01-2021
4th row25-04-2021
5th row14-06-2021

Common Values

ValueCountFrequency (%)
27-06-20205
 
0.3%
17-02-20165
 
0.3%
08-02-20184
 
0.3%
10-01-20164
 
0.3%
13-02-20134
 
0.3%
14-03-20164
 
0.3%
11-04-20184
 
0.3%
13-05-20164
 
0.3%
14-06-20124
 
0.3%
27-04-20184
 
0.3%
Other values (1102)1428
97.1%

Length

2021-11-26T14:41:09.148535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27-06-20205
 
0.3%
17-02-20165
 
0.3%
08-02-20184
 
0.3%
14-06-20124
 
0.3%
24-05-20164
 
0.3%
21-01-20204
 
0.3%
27-04-20184
 
0.3%
10-01-20164
 
0.3%
13-05-20164
 
0.3%
11-04-20184
 
0.3%
Other values (1102)1428
97.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Dept.Head
314 
Salary
297 
Work Environment
290 
Work Accident
285 
Better Opportunity
284 

Length

Max length18
Median length13
Mean length12.28911565
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalary
2nd rowWork Accident
3rd rowDept.Head
4th rowWork Accident
5th rowBetter Opportunity

Common Values

ValueCountFrequency (%)
Dept.Head314
21.4%
Salary297
20.2%
Work Environment290
19.7%
Work Accident285
19.4%
Better Opportunity284
19.3%

Length

2021-11-26T14:41:09.291808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:09.386100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
work575
24.7%
dept.head314
13.5%
salary297
12.8%
environment290
12.5%
accident285
12.2%
better284
12.2%
opportunity284
12.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mode_of_work
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
WFH
768 
OFFICE
702 

Length

Max length6
Median length3
Mean length4.432653061
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFFICE
2nd rowWFH
3rd rowWFH
4th rowOFFICE
5th rowWFH

Common Values

ValueCountFrequency (%)
WFH768
52.2%
OFFICE702
47.8%

Length

2021-11-26T14:41:09.542266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:09.635999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
wfh768
52.2%
office702
47.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Leaves
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.568707483
Minimum0
Maximum5
Zeros243
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:09.714160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.716170747
Coefficient of variation (CV)0.668106726
Kurtosis-1.284704635
Mean2.568707483
Median Absolute Deviation (MAD)1
Skewness-0.08739403717
Sum3776
Variance2.945242031
MonotonicityNot monotonic
2021-11-26T14:41:09.807876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4279
19.0%
3251
17.1%
5248
16.9%
0243
16.5%
1231
15.7%
2218
14.8%
ValueCountFrequency (%)
0243
16.5%
1231
15.7%
2218
14.8%
3251
17.1%
4279
19.0%
5248
16.9%
ValueCountFrequency (%)
5248
16.9%
4279
19.0%
3251
17.1%
2218
14.8%
1231
15.7%
0243
16.5%

Absenteeism
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
395 
2
373 
3
367 
0
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1395
26.9%
2373
25.4%
3367
25.0%
0335
22.8%

Length

2021-11-26T14:41:09.948420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:10.042195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1395
26.9%
2373
25.4%
3367
25.0%
0335
22.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
736 
True
734 
ValueCountFrequency (%)
False736
50.1%
True734
49.9%
2021-11-26T14:41:10.104674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Source_of_Hire
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Recruiter
390 
Job Event
372 
Walk-in
361 
Job Portal
347 

Length

Max length10
Median length9
Mean length8.744897959
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJob Event
2nd rowRecruiter
3rd rowJob Event
4th rowRecruiter
5th rowJob Event

Common Values

ValueCountFrequency (%)
Recruiter390
26.5%
Job Event372
25.3%
Walk-in361
24.6%
Job Portal347
23.6%

Length

2021-11-26T14:41:10.214028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:10.307715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
job719
32.8%
recruiter390
17.8%
event372
17.0%
walk-in361
16.5%
portal347
15.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Job_mode
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
FullTime
517 
Contract
482 
Part Time
471 

Length

Max length9
Median length8
Mean length8.320408163
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContract
2nd rowPart Time
3rd rowContract
4th rowFullTime
5th rowContract

Common Values

ValueCountFrequency (%)
FullTime517
35.2%
Contract482
32.8%
Part Time471
32.0%

Length

2021-11-26T14:41:10.432684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-26T14:41:10.510788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fulltime517
26.6%
contract482
24.8%
part471
24.3%
time471
24.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

day
Real number (ℝ≥0)

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.72585034
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:10.620136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median16
Q324
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.852365536
Coefficient of variation (CV)0.6690524016
Kurtosis-1.484419777
Mean14.72585034
Median Absolute Deviation (MAD)10
Skewness-0.01871014579
Sum21647
Variance97.06910666
MonotonicityNot monotonic
2021-11-26T14:41:10.745153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3114
 
7.8%
4102
 
6.9%
194
 
6.4%
593
 
6.3%
282
 
5.6%
678
 
5.3%
2761
 
4.1%
1760
 
4.1%
2459
 
4.0%
2556
 
3.8%
Other values (15)671
45.6%
ValueCountFrequency (%)
194
6.4%
282
5.6%
3114
7.8%
4102
6.9%
593
6.3%
678
5.3%
1349
3.3%
1450
3.4%
1547
3.2%
1643
 
2.9%
ValueCountFrequency (%)
3122
 
1.5%
3046
3.1%
2938
2.6%
2842
2.9%
2761
4.1%
2650
3.4%
2556
3.8%
2459
4.0%
2353
3.6%
2253
3.6%

year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.973469
Minimum1969
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:10.903873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1969
5-th percentile2001
Q12012
median2016
Q32018
95-th percentile2020
Maximum2021
Range52
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.220257498
Coefficient of variation (CV)0.00308854987
Kurtosis5.326754596
Mean2013.973469
Median Absolute Deviation (MAD)3
Skewness-1.918546639
Sum2960541
Variance38.69160334
MonotonicityNot monotonic
2021-11-26T14:41:11.062310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2016196
13.3%
2020171
11.6%
2018128
8.7%
2019127
8.6%
2011120
8.2%
2017110
 
7.5%
201490
 
6.1%
201282
 
5.6%
201380
 
5.4%
201576
 
5.2%
Other values (28)290
19.7%
ValueCountFrequency (%)
19691
 
0.1%
19811
 
0.1%
19841
 
0.1%
19852
 
0.1%
19871
 
0.1%
19885
0.3%
19893
0.2%
19903
0.2%
19911
 
0.1%
19922
 
0.1%
ValueCountFrequency (%)
202144
 
3.0%
2020171
11.6%
2019127
8.6%
2018128
8.7%
2017110
7.5%
2016196
13.3%
201576
 
5.2%
201490
6.1%
201380
5.4%
201282
5.6%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.692517007
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-11-26T14:41:11.191429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.941339086
Coefficient of variation (CV)0.6268147951
Kurtosis0.2399304571
Mean4.692517007
Median Absolute Deviation (MAD)2
Skewness0.9271774104
Sum6898
Variance8.651475621
MonotonicityNot monotonic
2021-11-26T14:41:11.296915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3222
15.1%
5210
14.3%
4204
13.9%
2195
13.3%
6185
12.6%
1176
12.0%
1267
 
4.6%
747
 
3.2%
844
 
3.0%
1144
 
3.0%
Other values (2)76
 
5.2%
ValueCountFrequency (%)
1176
12.0%
2195
13.3%
3222
15.1%
4204
13.9%
5210
14.3%
6185
12.6%
747
 
3.2%
844
 
3.0%
939
 
2.7%
1037
 
2.5%
ValueCountFrequency (%)
1267
 
4.6%
1144
 
3.0%
1037
 
2.5%
939
 
2.7%
844
 
3.0%
747
 
3.2%
6185
12.6%
5210
14.3%
4204
13.9%
3222
15.1%

Interactions

2021-11-26T14:40:58.902380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:28.389019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.825415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:33.114344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:35.630124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.729301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:40.131115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:42.455448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:44.639154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:47.052444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:49.309681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:51.916141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:54.199601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:56.705359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.038832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:28.556630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.974022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:33.261760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:35.764204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.910066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:40.282861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:42.598940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:44.910759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:47.221389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:49.457546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:52.052738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:54.346187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:56.852608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.193643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:28.717201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:31.139575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:33.430477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:35.915760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:38.070835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:40.444990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:42.751398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:45.072756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:47.382493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:49.639060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:52.233256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:54.528737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.016566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.343378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:28.876775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:31.316105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:33.603717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:36.079321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:38.250149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:40.618932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:42.916383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:45.243493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:47.551054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:49.819418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:52.408762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:54.706137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.185390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.505858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:29.127104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:31.469692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:33.762975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:36.220943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:38.397225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:40.780501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:43.058029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:45.395722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:47.697845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:50.110572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:52.553203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:54.852448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.327488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.660108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:29.293886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:31.631296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:33.935478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:36.377303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:38.564157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:40.951570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:43.214384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:45.552910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:47.854765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:50.289792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:52.733720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:55.015130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.477790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.817300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:29.467165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:31.797686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:34.131953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:36.544856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:38.731186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:41.128236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:43.388445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:45.742231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:48.022707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:50.471133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:52.903431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:55.179356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.645198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:59.971199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:29.651633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:31.977972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:34.312470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:36.699247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:38.894713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:41.274851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:43.543360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:45.905690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:48.180548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:50.658517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:53.066994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:55.366854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.805594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:41:00.135213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:29.830156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:32.150778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:34.625593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:36.861134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:39.067512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:41.469829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:43.711058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:46.069070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:48.348516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:50.855984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:53.265424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:55.674341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:57.972354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:41:00.299294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.043584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:32.329299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:34.788310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.001804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:39.238549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:41.642762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:43.871013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:46.252584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:48.517981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:51.025502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:53.412889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:55.863870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:58.136090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:41:00.461627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.202566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:32.502791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:34.972066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.168564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:39.400776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:41.818162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:44.033665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:46.417657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:48.695155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:51.227965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:53.576864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:56.050163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:58.292381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:41:00.607208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.358404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:32.653374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:35.138355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.313972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:39.549890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:41.978195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:44.173011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:46.579728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:48.834072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:51.399548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:53.723194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:56.201347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:58.431779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:41:00.877236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.531899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:32.809008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:35.307510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.464215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:39.828269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:42.142286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:44.339647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:46.748915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:49.000827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:51.558624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:53.875163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:56.382862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:58.598897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:41:01.023891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:30.678810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:32.964348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:35.467785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:37.605570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:39.977981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:42.300206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:44.471722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:46.909938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:49.150434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:51.744694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:54.050692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:56.550958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-26T14:40:58.744061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-11-26T14:41:11.423684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-26T14:41:11.798552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-26T14:41:12.157839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-26T14:41:12.657723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-26T14:41:13.032636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-26T14:41:01.371323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-26T14:41:03.278087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeAttritionBusinessTravelDepartmentDistanceFromHomeGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerHigher_EducationDate_of_HireStatus_of_leavingMode_of_workLeavesAbsenteeismWork_accidentSource_of_HireJob_modedayyearmonth
037YesTravel_RarelyResearch & Development2Male21Laboratory Technician3Single20906Yes153073000Graduation21-01-2021SalaryOFFICE42NoJob EventContract2120211
121NoTravel_RarelyResearch & Development15Male31Research Scientist4Single12321No143006000Graduation13-03-2021Work AccidentWFH52NoRecruiterPart Time1320213
245NoTravel_RarelyResearch & Development6Male33Research Director1Married132454Yes1430173000Post-Graduation23-01-2021Dept.HeadWFH13NoJob EventContract2320211
323NoTravel_RarelySales2Male31Sales Representative1Divorced23223No133133000PHD25-04-2021Work AccidentOFFICE10YesRecruiterFullTime2520214
422NoTravel_RarelyResearch & Development15Female31Laboratory Technician4Single28711No153015000PHD14-06-2021Better OpportunityWFH52NoJob EventContract1420216
519YesTravel_RarelySales22Male31Sales Representative3Single16751Yes193002000PHD14-04-2021Work AccidentWFH11YesJob PortalPart Time1420214
619YesTravel_FrequentlySales1Female11Sales Representative1Single23250No214015000PHD12-01-2021Work AccidentWFH22NoWalk-inContract1202112
728YesTravel_RarelyResearch & Development2Male31Laboratory Technician3Single34852No113055000Post-Graduation30-05-2021Work EnvironmentWFH02NoWalk-inContract3020215
829NoTravel_RarelySales2Male22Sales Executive2Married66442No1932102000Graduation28-02-2021Better OpportunityOFFICE52NoWalk-inPart Time2820212
918YesTravel_RarelyResearch & Development3Male31Laboratory Technician3Single14201No133002000PHD06-05-2021Work EnvironmentWFH52NoWalk-inFullTime520216

Last rows

AgeAttritionBusinessTravelDepartmentDistanceFromHomeGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerHigher_EducationDate_of_HireStatus_of_leavingMode_of_workLeavesAbsenteeismWork_accidentSource_of_HireJob_modedayyearmonth
146052NoTravel_RarelyResearch & Development1Male25Manager3Married199990No143134533119PHD14-03-1988Better OpportunityWFH50NoRecruiterFullTime1419883
146152NoNon-TravelSales2Male25Manager3Single190681Yes1830332331512Graduation12-05-1988SalaryWFH43YesJob EventPart Time5198812
146255NoNon-TravelResearch & Development8Male24Healthcare Representative2Divorced135771Yes15313433315012th09-04-1988SalaryWFH31NoWalk-inPart Time419889
146351NoTravel_RarelyHuman Resources5Male34Manager2Divorced140261Yes113133233010Post-Graduation04-04-1988Better OpportunityOFFICE41YesJob PortalContract419884
146453YesTravel_RarelyResearch & Development2Female23Manufacturing Director4Married101690No16313443319Post-Graduation20-06-1988Work AccidentWFH40YesRecruiterContract2019886
146552NoTravel_RarelySales3Male24Manager1Married168561No113034334116Post-Graduation05-06-1987SalaryOFFICE32NoJob PortalPart Time619875
146655NoTravel_RarelyResearch & Development1Male35Manager1Single190450Yes143037236413Post-Graduation20-01-1985Work AccidentWFH11NoWalk-inFullTime2019851
146755NoTravel_RarelySales26Male25Manager4Married195861No214136336213Post-Graduation17-02-1985Work AccidentOFFICE21NoRecruiterPart Time1719852
146858NoTravel_RarelySales10Male34Sales Executive3Single138720No13303813718PHD29-06-1984Work EnvironmentWFH22YesJob EventPart Time2919846
146958YesTravel_RarelyResearch & Development23Female33Healthcare Representative4Married103121No12314034015612th08-02-1981Work EnvironmentWFH43YesJob PortalFullTime219818